import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
import re
import math
import numpy as np
import traceback
from prompt_rewrite import rewrite
import hashlib

###################################
base_model = "Qwen/Qwen-Image"

# Lightning LoRA info (no global state)

LIGHTNING_LORA_REPO = "lightx2v/Qwen-Image-Lightning"
LIGHTNING_LORA_WEIGHT = "Qwen-Image-Lightning-4steps-V2.0.safetensors"
LIGHTNING8_LORA_WEIGHT = "Qwen-Image-Lightning-8steps-V2.0.safetensors"
LIGHTNING_FP8_4STEPS_LORA_WEIGHT = "Qwen-Image-fp8-e4m3fn-Lightning-4steps-V1.0-bf16.safetensors"

###################################


def apply_aspect_ratio(ratio):
    sizes = {
        "1:1": (1024, 1024),
        "16:9": (1365, 768),
        "9:16": (768, 1365),
        "3:2": (1254, 836),
        "2:3": (836, 1254),
        "3:1": (1774, 591),
        "2:1": (1448, 724),
    }
    return sizes.get(ratio, (1024, 1024))

DEFAULT_ASPECT_RATIO = "16:9"

# ✅ NUEVO: importar optimización avanzada tipo Qwen-Image-MultipleAngles
#from optimization import optimize_pipeline_

LORAS_CACHE = {
    "data": [],
    "last_hash": None,
}
def load_loras_hot():
    """Load loras.json and detect changes."""
    path = hf_hub_download(
        repo_id="lichorosario/qwen-image-lora-dlc-v3",
        filename="loras.json",
        repo_type="space",
    )

    with open(path, "r", encoding="utf-8") as f:
        raw = f.read()

    current_hash = hashlib.sha256(raw.encode("utf-8")).hexdigest()

    if current_hash != LORAS_CACHE["last_hash"]:
        LORAS_CACHE["data"] = json.loads(raw)
        LORAS_CACHE["last_hash"] = current_hash
        print("🔁 LoRA config updated")

    return LORAS_CACHE["data"]

    
# Load LoRAs from JSON file
def load_loras_from_file():
    """Load LoRA configurations from external JSON file."""
    try:
        with open('loras.json', 'r', encoding='utf-8') as f:
            return json.load(f)
    except FileNotFoundError:
        print("Warning: loras.json file not found. Using empty list.")
        return []
    except json.JSONDecodeError as e:
        print(f"Error parsing loras.json: {e}")
        return []


# Load the LoRAs
#loras = load_loras_from_file()
loras = load_loras_hot()


# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"


# Scheduler configuration from the Qwen-Image-Lightning repository
scheduler_config = {
    "base_image_seq_len": 256,
    "base_shift": math.log(3),
    "invert_sigmas": False,
    "max_image_seq_len": 8192,
    "max_shift": math.log(3),
    "num_train_timesteps": 1000,
    "shift": 1.0,
    "shift_terminal": None,
    "stochastic_sampling": False,
    "time_shift_type": "exponential",
    "use_beta_sigmas": False,
    "use_dynamic_shifting": True,
    "use_exponential_sigmas": False,
    "use_karras_sigmas": False,
}


scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)

pipe = DiffusionPipeline.from_pretrained(
    base_model, scheduler=scheduler, torch_dtype=dtype
).to(device)



"""
# ✅ NUEVO BLOQUE: aplicar AOT optimization (igual que Qwen-Image-MultipleAngles)
try:
    example_args = (
        "a cute cat in a spacesuit",
    )
    example_kwargs = dict(
        num_inference_steps=4,
        true_cfg_scale=3.5,
        width=1024,
        height=1024,
        num_images_per_prompt=1,
    )
    optimize_pipeline_(pipe, *example_args, **example_kwargs)
    print("✅ Transformer AOT optimization complete.")
except Exception as e:
    print(f"⚠️ AOT optimization skipped: {e}")
"""




MAX_SEED = np.iinfo(np.int32).max


### MODIFICACIÓN 1: AÑADIR FUNCIONES PARA GESTIONAR EL HISTORIAL ###
def update_history(new_images, history):
    """Añade las nuevas imágenes generadas al principio de la lista del historial."""
    if history is None:
        history = []
    if new_images is not None and len(new_images) > 0:
        updated_history = new_images + history
        return updated_history[:24]
    return history


def clear_history():
    """Devuelve una lista vacía para limpiar la galería de historial."""
    return []
### FIN DE LA MODIFICACIÓN 1 ###




class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name


    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")




def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    
    examples_list = []
    try:
        model_card = ModelCard.load(lora_repo)
        widget_data = model_card.data.get("widget", [])
        if widget_data and len(widget_data) > 0:
            for example in widget_data[:4]:
                if "output" in example and "url" in example["output"]:
                    image_url = f"https://huggingface.co/{lora_repo}/resolve/main/{example['output']['url']}"
                    prompt_text = example.get("text", "")
                    examples_list.append([prompt_text])
    except Exception as e:
        print(f"Could not load model card for {lora_repo}: {e}")
    
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
        gr.update(interactive=True)
    )


def handle_speed_mode(speed_mode):
    """Update UI based on speed/quality toggle."""
    if speed_mode == "light 4":
        return gr.update(value="Light mode (4 steps) selected"), 4, 1.0
    elif speed_mode == "light 4 fp8":
        return gr.update(value="Light mode (4 steps fp8) selected"), 4, 1.0
    elif speed_mode == "light 8":
        return gr.update(value="Light mode (8 steps) selected"), 8, 1.0
    elif speed_mode == "Wuli-art":
        return gr.update(value="Light mode (4 steps) Wuli-art selected"), 4, 1.0
    else: 
        return gr.update(value="Normal quality (45 steps) selected"), 45, 3.5


@spaces.GPU(duration=70)
def generate_image(
    prompt_mash,
    steps,
    seed,
    cfg_scale,
    width,
    height,
    lora_scale,
    negative_prompt="",
    num_images=1,
    prompt_enhance=False,
):
    pipe.to("cuda")
#    if negative_prompt == '':
#        negative_prompt =  "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感。构图混乱。文字模糊，扭曲。"

        
    if prompt_enhance:
        with calculateDuration("Enjancing prompt"):
            print(f"Calling pipeline with prompt: '{prompt_mash}'")
            prompt_mash = rewrite(prompt_mash)

    seeds = [seed + (i * 100) for i in range(num_images)]
    generators = [torch.Generator(device="cuda").manual_seed(s) for s in seeds]
    
    images = []

    with calculateDuration("Generating images (sequential)"):
        for i in range(num_images):
            current_seed = seed + (i * 100)
            generator = torch.Generator(device="cuda").manual_seed(current_seed)
    
            result = pipe(
                prompt=prompt_mash,
                negative_prompt=negative_prompt,
                num_inference_steps=steps,
                true_cfg_scale=cfg_scale,
                width=width,
                height=height,
                num_images_per_prompt=1,
                generator=generator,
            )
    
            images.append((result.images[0], current_seed))
    return images



def generate_images_for_prompts(
    prompts,
    negative_prompt,
    steps,
    seed,
    cfg_scale,
    width,
    height,
    quantity,  # ✅ FIX: ahora entra como parámetro
    prompt_enhance=False,
):
    pipe.to("cuda")
#    if negative_prompt == '':
#        negative_prompt =  "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感。构图混乱。文字模糊，扭曲。"
    
    images = []

    for prompt in prompts:
        current_seed = seed
        if prompt_enhance:
            prompt = rewrite(prompt)

        # ✅ FIX: quantity ya no es el componente global; es un int real
        for _ in range(int(quantity)):
            generator = torch.Generator(device="cuda").manual_seed(current_seed)
    
            result = pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=steps,
                true_cfg_scale=cfg_scale,
                width=width,
                height=height,
                num_images_per_prompt=1,
                generator=generator,
            )
    
            images.append((result.images[0], current_seed))
            current_seed += 100  # separación segura

    return images



@spaces.GPU(duration=70)
def run_lora_multi(
    prompt_1, prompt_2, prompt_3, prompt_4,
    negative_prompt,
    cfg_scale, steps,
    selected_index,
    randomize_seed, seed,
    width, height,
    lora_scale,
    speed_mode,
    quality_multiplier,
    quantity,  # se ignora acá (pero ahora lo usamos bien)
    history,
    prompt_enhance=False,
    progress=gr.Progress(track_tqdm=True)
):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.")

    prompts = [
        p.strip() for p in [prompt_1, prompt_2, prompt_3, prompt_4]
        if p and p.strip()
    ]

    if not prompts:
        raise gr.Error("You must fill at least one prompt.")

    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]

    # aplicar trigger word por prompt
    final_prompts = []
    for p in prompts:
        if trigger_word:
            if selected_lora.get("trigger_position") == "append":
                final_prompts.append(f"{p} {trigger_word}")
            else:
                final_prompts.append(f"{trigger_word} {p}")
        else:
            final_prompts.append(p)

    # limpiar LoRAs previas
    pipe.unload_lora_weights()

    # 🔥 CARGA DE LORAs (UNA SOLA VEZ)
    if speed_mode == "light 4":
        pipe.load_lora_weights(
            LIGHTNING_LORA_REPO,
            weight_name=LIGHTNING_LORA_WEIGHT,
            adapter_name="lightning"
        )
        pipe.load_lora_weights(
            lora_path,
            weight_name=selected_lora.get("weights"),
            adapter_name="style"
        )
        pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])

    elif speed_mode == "light 8":
        pipe.load_lora_weights(
            LIGHTNING_LORA_REPO,
            weight_name=LIGHTNING8_LORA_WEIGHT,
            adapter_name="lightning"
        )
        pipe.load_lora_weights(
            lora_path,
            weight_name=selected_lora.get("weights"),
            adapter_name="style"
        )
        pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
    
    elif speed_mode == "Wuli-art":
        with calculateDuration("Loading Lightning LoRA and style LoRA"):
            pipe.load_lora_weights(
                'Wuli-Art/Qwen-Image-2512-Turbo-LoRA', 
                weight_name='Wuli-Qwen-Image-2512-Turbo-LoRA-4steps-V2.0-bf16.safetensors',
                adapter_name="lightning"
            )
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])

    elif speed_mode == "light 4 fp8":
        with calculateDuration("Loading Lightning LoRA and style LoRA"):
            pipe.load_lora_weights(
                LIGHTNING_LORA_REPO, 
                weight_name=LIGHTNING_FP8_4STEPS_LORA_WEIGHT,
                adapter_name="lightning"
            )
            weight_name = selected_lora.get("weights", None)
            pipe.load_lora_weights(
                lora_path, 
                weight_name=weight_name, 
                low_cpu_mem_usage=True,
                adapter_name="style"
            )
            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])

    
    else:
        pipe.load_lora_weights(
            lora_path,
            weight_name=selected_lora.get("weights"),
            adapter_name="style"
        )
        pipe.set_adapters(["style"], adapter_weights=[lora_scale])

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    multiplier = float(quality_multiplier.replace("x", ""))
    width = int(width * multiplier)
    height = int(height * multiplier)

    # ✅ FIX: quantity viene como index 0..3 (por type="index"), convertimos a 1..4
    real_quantity = int(quantity) + 1


    if (history is None):
        history = []
    gallery_images = []
        
    for prompt in prompts:
        current_seed = seed
        if prompt_enhance:
            prompt = rewrite(prompt)
    
        # ✅ FIX: quantity ya no es el componente global; es un int real
        for _ in range(real_quantity):
            generator = torch.Generator(device="cuda").manual_seed(current_seed)
    
            result = pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=steps,
                true_cfg_scale=cfg_scale,
                width=width,
                height=height,
                num_images_per_prompt=1,
                generator=generator,
            )
            
            img = result.images[0]
            imgtuple = (img, str(current_seed))
#            images.append(imgtuple)
            gallery_images.append(imgtuple)
            
            # history persistente (acumula)
            history = [(img, str(current_seed))] + history
            history = history[:24]

            yield gallery_images, history, history, seed
            
            current_seed += 100  # separación segura
    
    #return images
    
    #images = generate_images_for_prompts(
    #    prompts=final_prompts,
    #    negative_prompt=negative_prompt,
    #    steps=steps,
    #    seed=seed,
    #    cfg_scale=cfg_scale,
    #    width=width,
    #    height=height,
    #    quantity=real_quantity,  # ✅ FIX: ahora se pasa
    #    prompt_enhance=prompt_enhance,
    #)

    #gallery_images = [(img, str(s)) for img, s in images]
    #return gallery_images, seed



# ... (El resto de las funciones como get_huggingface_safetensors, check_custom_model, etc., permanecen sin cambios) ...
def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) != 2:
        raise Exception("Invalid Hugging Face repository link format.")
    print(f"Repository attempted: {split_link}")
    model_card = ModelCard.load(link)
    base_model = model_card.data.get("base_model")
    print(f"Base model: {base_model}")
    acceptable_models = {
        "Qwen/Qwen-Image",
        "Qwen/Qwen-Image-2512",
    }
    models_to_check = base_model if isinstance(base_model, list) else [base_model]
    if not any(model in acceptable_models for model in models_to_check):
        raise Exception("Not a Qwen-Image LoRA!")
    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
    trigger_word = model_card.data.get("instance_prompt", "")
    image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
    fs = HfFileSystem()
    try:
        list_of_files = fs.ls(link, detail=False)
        safetensors_name = None
        for file in list_of_files:
            filename = file.split("/")[-1]
            if filename.endswith(".safetensors"):
                safetensors_name = filename
                break
        if not safetensors_name:
            raise Exception("No valid *.safetensors file found in the repository.")
    except Exception as e:
        print(e)
        raise Exception("You didn't include a valid Hugging Face repository with a *.safetensors LoRA")
    return split_link[1], link, safetensors_name, trigger_word, image_url


def check_custom_model(link):
    print(f"Checking a custom model on: {link}")
    if link.endswith('.safetensors'):
        if 'huggingface.co' in link:
            parts = link.split('/')
            try:
                hf_index = parts.index('huggingface.co')
                username = parts[hf_index + 1]
                repo_name = parts[hf_index + 2]
                repo = f"{username}/{repo_name}"
                safetensors_name = parts[-1]
                try:
                    model_card = ModelCard.load(repo)
                    trigger_word = model_card.data.get("instance_prompt", "")
                    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
                    image_url = f"https://huggingface.co/{repo}/resolve/main/{image_path}" if image_path else None
                except:
                    trigger_word = ""
                    image_url = None
                return repo_name, repo, safetensors_name, trigger_word, image_url
            except:
                raise Exception("Invalid safetensors URL format")
    if link.startswith("https://"):
        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)


def add_custom_lora(custom_lora):
    global loras
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            model_card_examples = ""
            try:
                model_card = ModelCard.load(repo)
                widget_data = model_card.data.get("widget", [])
                if widget_data and len(widget_data) > 0:
                    examples_html = '<div style="margin-top: 10px;">'
                    examples_html += '<h4 style="margin-bottom: 8px; font-size: 0.9em;">Sample Images:</h4>'
                    examples_html += '<div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 8px;">'
                    for i, example in enumerate(widget_data[:4]):
                        if "output" in example and "url" in example["output"]:
                            image_url = f"https://huggingface.co/{repo}/resolve/main/{example['output']['url']}"
                            caption = example.get("text", f"Example {i+1}")
                            examples_html += f'''
                            <div style="text-align: center;">
                                <img src="{image_url}" style="width: 100%; height: auto; border-radius: 4px;" />
                                <p style="font-size: 0.7em; margin: 2px 0;">{caption[:30]}{'...' if len(caption) > 30 else ''}</p>
                            </div>
                            '''
                    examples_html += '</div></div>'
                    model_card_examples = examples_html
            except Exception as e:
                print(f"Could not load model card examples for custom LoRA: {e}")
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
              {model_card_examples}
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if existing_item_index is None:
                new_item = {"image": image, "title": title, "repo": repo, "weights": path, "trigger_word": trigger_word}
                print(new_item)
                loras.append(new_item)
                existing_item_index = len(loras) - 1
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word, gr.update(interactive=True)
        except Exception as e:
            full_traceback = traceback.format_exc()
            print(f"Full traceback:\n{full_traceback}")
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-Qwen-Image LoRA, this was the issue: {e}")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-Qwen-Image LoRA"), gr.update(visible=True), gr.update(), "", None, "", gr.update(interactive=False)
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "", gr.update(interactive=False)


def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "", gr.update(interactive=False)


def reload_loras_gallery():
    global loras
    loras = load_loras_hot()

    gallery_items = [
        (item["image"], item.get("title") or item.get("name"))
        for item in loras
        if item.get("image")
    ]

    return gr.update(value=gallery_items)



def init(speed_mode, aspect_ratio):
    loras_result = reload_loras_gallery()
    speed_mode_result = handle_speed_mode(speed_mode)
    aspect_ratio_result = apply_aspect_ratio(aspect_ratio)
    return (
        *speed_mode_result,
        *aspect_ratio_result,
        loras_result
    )

css = '''
#gen_btn{height: 100%}
#gen_column{align-self: stretch}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#speed_status{padding: .5em; border-radius: 5px; margin: 1em 0}
'''


with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 60)) as app:
    title = gr.HTML(
        """<h1 style=\"color:#644fea\">Qwen-Image</h1>        
        <h3 style=\"margin-top: -10px\">LoRA🦜 ChoquinLabs Explorer</h3>""",
        elem_id="title",
    )
    
    selected_index = gr.State(None)
    
    with gr.Row():
        with gr.Column(scale=3):
    
            prompt_1 = gr.Textbox(label="Prompt 1", lines=1)
            prompt_2 = gr.Textbox(label="Prompt 2", lines=1)
            prompt_3 = gr.Textbox(label="Prompt 3", lines=1)
            prompt_4 = gr.Textbox(label="Prompt 4", lines=1)
            
            negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="Optional: what to avoid")
            prompt_enhance = gr.Checkbox(label="Prompt Enhance", value=False)
        with gr.Column(scale=1, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn", interactive=False)


    
    with gr.Row():
        with gr.Column():
            selected_info = gr.Markdown("")
            examples_component = gr.Examples(examples=[], inputs=[prompt_1], label="Sample Prompts", visible=False)
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery",
                show_share_button=False
            )
            reload_btn = gr.Button("🔄 Reload LoRAs")            
            
            
            with gr.Group():
                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="username/qwen-image-custom-lora")
                gr.Markdown("[Check Qwen-Image LoRAs](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list")
            custom_lora_info = gr.HTML(visible=False)
            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
        
        with gr.Column():
            result = gr.Gallery(label="Generated Images", show_label=True, elem_id="result_gallery")
            history_state = gr.State([]) 
            ### MODIFICACIÓN 2: AÑADIR LOS COMPONENTES DE LA UI DEL HISTORIAL ###
            with gr.Group():
                with gr.Row():
                    gr.Markdown("### 📜 History")
                    clear_history_button = gr.Button("🗑️ Clear History", size="sm")


                history_gallery = gr.Gallery(
                    label="Generation History",
                    show_label=False,
                    columns=4,
                    object_fit="contain",
                    height="auto",
                    interactive=False
                )
            ### FIN DE LA MODIFICACIÓN 2 ###
            
            with gr.Row():
                with gr.Column():
                    speed_mode = gr.Radio(
                        label="Generation Mode",
                        choices=["light 4", "light 8", "Wuli-art", "light 4 fp8", "normal"],
                        value="light 4",
                        info="'light' modes use Lightning LoRA for faster generation"
                    )
                with gr.Column():
                    quantity = gr.Radio(
                        label="Quantity",
                        choices=["1", "2", "3", "4"],
                        value="1",
                        type="index"
                    )
            
            speed_status = gr.Markdown("Quality mode active", elem_id="speed_status")

            with gr.Row():
                aspect_ratio = gr.Radio(
                    label="Aspect Ratio",
                    choices=["1:1", "16:9", "9:16", "3:2", "2:3", "3:1", "2:1"],
                    value="16:9"
                )


            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=1920,
                    step=1,
                    value=1920
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=1920,
                    step=1,
                    value=1080
                )


            with gr.Row():
                quality_multiplier = gr.Radio(
                    label="Quality (Size Multiplier)",
                    choices=["0.5x", "0.75x", "1x", "1.5x", "2x"],
                    value="1x"
                )


    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(
                        label="Guidance Scale (True CFG)", 
                        minimum=1.0, 
                        maximum=5.0, 
                        step=0.1, 
                        value=3.5,
                        info="Lower for speed mode, higher for quality"
                    )
                    steps = gr.Slider(
                        label="Steps", 
                        minimum=4, 
                        maximum=50, 
                        step=1, 
                        value=45,
                        info="Automatically set by speed mode"
                    )
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=1.0)


    # Event handlers
    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt_1, selected_info, selected_index, width, height, generate_button]
    )
    
    speed_mode.change(
        handle_speed_mode,
        inputs=[speed_mode],
        outputs=[speed_status, steps, cfg_scale]
    )
    
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt_1, generate_button]
    )
    
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora, generate_button]
    )
    
    ### MODIFICACIÓN 3: CONECTAR LOS EVENTOS DEL HISTORIAL ###
    # Evento principal de generación
    generate_event = gr.on(
        triggers=[generate_button.click, prompt_1.submit],
    
        fn=run_lora_multi,
        inputs=[
            prompt_1, prompt_2, prompt_3, prompt_4,
            negative_prompt,
            cfg_scale, steps, selected_index,
            randomize_seed, seed,
            width, height, lora_scale,
            speed_mode, quality_multiplier,
            quantity,
            history_state,
            prompt_enhance
        ],
        outputs=[result, history_gallery, history_state, seed]
    )


    
    # Encadenar la actualización del historial para que se ejecute DESPUÉS de la generación
#    generate_event.then(
#        fn=update_history,
#        inputs=[result, history_gallery],
#        outputs=history_gallery,
#        show_api=False
#    )


    # Evento para el botón de limpiar historial
    clear_history_button.click(
        fn=clear_history,
        inputs=None,
        outputs=[history_state, history_gallery],
        show_api=False
    )
    ### FIN DE LA MODIFICACIÓN 3 ###

    aspect_ratio.change(
        fn=apply_aspect_ratio,
        inputs=[aspect_ratio],
        outputs=[width, height]
    )

    reload_btn.click(
        fn=reload_loras_gallery,
        outputs=gallery,
    )
 
    app.load(
        fn=init,
        inputs=[gr.State("light 4"), gr.State(DEFAULT_ASPECT_RATIO)],
        outputs=[speed_status, steps, cfg_scale, width, height, gallery]
    )


app.queue()
app.launch()